Building Enhanced Outcomes to Support Patients with Cancer: A Constructivist Grounded Theory of Oncology Healthcare Provider Experiences Working Within Canadian Urgent Cancer Clinics
Bibliographic record
Abstract
Patients with acute cancer symptoms (e.g., fevers, gastrointestinal disturbances, or uncontrolled pain) from ambulatory cancer centres predominantly rely on emergency departments (EDs) for assessment and treatment. However, this model of care is no longer sustainable and emphasizes healthcare system inefficiencies. The advent of urgent cancer clinics (UCCs) allows patients to have these symptoms treated by oncology experts within ambulatory cancer centres. Unfortunately, limited research on UCCs both operationally and experientially makes it difficult for others to adopt this new model of care. A constructivist grounded theory study was conducted to explore the processes and experiences of oncology healthcare providers (i.e., registered nurses, nurse practitioners, and physicians) in managing outpatient acute cancer symptoms within Canadian UCCs. Ten participants were recruited and interviewed from four Canadian UCCs. Grounded theory coding allowed categories to naturally emerge from the data and led to the co-construction of a substantive theory - Building Enhanced Outcomes to Support Patients with Cancer. This theory is comprised of three major categories and eight subcategories all working towards a common goal, the core category of Building Enhanced Outcomes. Findings from this study offer many new insights and practice implications related to managing outpatient acute cancer symptoms both within specialized UCCs and generalized ambulatory cancer centres.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".